scholarly journals The Prefiltering Techniques in Emotion Based Place Recommendation Derived by User Reviews

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
U. A. Piumi Ishanka ◽  
Takashi Yukawa
Keyword(s):  
User Satisfaction ◽  
Recommendation System ◽  
Semantic Analysis ◽  
Contextual Information ◽  
User Requirements ◽  
Context Aware ◽  
Tourist Destination ◽  
Analysis Techniques ◽  
Music And Emotion ◽  
Contextual Parameter

Context-aware recommendation systems attempt to address the challenge of identifying products or items that have the greatest chance of meeting user requirements by adapting to current contextual information. Many such systems have been developed in domains such as movies, books, and music, and emotion is a contextual parameter that has already been used in those fields. This paper focuses on the use of emotion as a contextual parameter in a tourist destination recommendation system. We developed a new corpus that incorporates the emotion parameter by employing semantic analysis techniques for destination recommendation. We review the effectiveness of incorporating emotion in a recommendation process using prefiltering techniques and show that the use of emotion as a contextual parameter for location recommendation in conjunction with collaborative filtering increases user satisfaction.

scholarly journals Design and Development of a Context-aware Personalized Recommendation System: Mobile and Web Application

2017 ◽  
Author(s):  
Sara Saeedi ◽  
Xueyang Zou ◽  
Mariel Gonzales ◽  
Steve Liang
Keyword(s):  
Web Application ◽  
Management Practices ◽  
Recommendation System ◽  
Contextual Information ◽  
User Preferences ◽  
Personalized Recommendation ◽  
Context Information ◽  
Context Aware ◽  
Context Reasoning ◽  
Past Behavior

The ubiquity of mobile sensors (such as GPS, accelerometer and gyroscope) together with increasing computational power have enabled an easier access to contextual information, which proved its value in next generation of the recommender applications. The importance of contextual information has been recognized by researchers in many disciplines, such as ubiquitous and mobile computing, to filter the query results and provide recommendations based on different user status. A context-aware recommendation system (CoARS) provides a personalized service to each individual user, driven by his or her particular needs and interests at any location and anytime. Therefore, a contextual recommendation system changes in real time as a user’s circumstances changes. CoARS is one of the major applications that has been refined over the years due to the evolving geospatial techniques and big data management practices. In this paper, a CoARS is designed and implemented to combine the context information from smartphones’ sensors and user preferences to improve efficiency and usability of the recommendation. The proposed approach combines user’s context information (such as location, time, and transportation mode), personalized preferences (using individuals past behavior), and item-based recommendations (such as item’s ranking and type) to personally filter the item list. The context-aware methodology is based on preprocessing and filtering of raw data, context extraction and context reasoning. This study examined the application of such a system in recommending a suitable restaurant using both web-based and android platforms. The implemented system uses CoARS techniques to provide beneficial and accurate recommendations to the users. The capabilities of the system is evaluated successfully with recommendation experiment and usability test.

A State-of-the-Art Survey on Context-Aware Recommender Systems and Applications

2021 ◽  
Vol 12 (3) ◽  
pp. 1-20
Author(s):  
Quang-Hung Le ◽  
Son-Lam Vu ◽  
Thi-Kim-Phuong Nguyen ◽  
Thi-Xinh Le
Keyword(s):  
Recommender Systems ◽  
Recommendation System ◽  
Contextual Information ◽  
Information Filtering ◽  
Weather Conditions ◽  
Research Literature ◽  
Future Research ◽  
Context Aware ◽  
Good Tool ◽  
Advantages And Disadvantages

In the digital transformation era, increasingly more individuals and organizations use or create services in digital spaces. Many business transactions have been moving from the offline to online mode. For example, sellers intend to introduce their products on e-commerce platforms rather than display them on store shelves as in traditional business. Although this new format business has advantages, such as more space for product displays, more efficient searches for a specific item, and providing a good tool for both buyers and sellers to manage their products, it is also accompanied by the obviously important problem that users are confused when choosing an appropriate item due to a large amount of information. For this reason, the need for a recommendation system appears. Informally, a recommender system is similar to an information filtering system that helps identify a set of items that best satisfy users' demands based on their preference profiles. The integration of contextual information (e.g., location, weather conditions, and user's mood) into recommender systems to improve their performance has recently received considerable attention in the research literature. However, incorporating such contextual information into recommendation models is a challenging task because of the increase in both the dimensionality and sparsity of the model. Different approaches with their own advantages and disadvantages have been proposed. This paper provides a comprehensive survey on context-aware recommender systems in recent years. In particular, the authors pay more attention to journal and conference proceedings papers published from 2016 to 2020. In addition, this paper also presents open issues for context-aware recommender systems and discuss promising directions for future research.

scholarly journals Contexts in a paper recommendation system with collaborative filtering

2012 ◽  
Vol 13 (5) ◽  
pp. 56 ◽  
Author(s):  
Pinata Winoto ◽  
Tiffany Y. Tang ◽  
Gordon I. McCalla
Keyword(s):  
Graduate Students ◽  
Collaborative Filtering ◽  
Undergraduate Students ◽  
Information Seeking ◽  
Recommendation System ◽  
Contextual Information ◽  
Learning Goals ◽  
Context Aware ◽  
Learning Groups ◽  
Filtering Techniques

Making personalized paper recommendations to users in an educational domain is not a trivial task of simply matching users’ interests with a paper topic. Therefore, we proposed a context-aware multidimensional paper recommendation system that considers additional user and paper features. Earlier experiments on experienced graduate students demonstrated the significance of this approach using modified collaborative filtering techniques. However, two key issues remain: (1) How would the modified filtering perform when target users are inexperienced undergraduate students who have a different pedagogical background and contextual information-seeking goals, such as task- and course-related goals, from those of graduate students?; (2) Should we combine graduates and undergraduates in the same pool, or should we separate them? We conducted two studies aimed at addressing these issues and they showed that (1) the system can be effectively used for inexperienced learners; (2) recommendations are less effective for different learning groups (with different pedagogical features and learning goals) than they are for the same learning groups. Based on the results obtained from these studies, we suggest several context-aware filtering techniques for different learning scenarios.<br /><br />

scholarly journals Differential Privacy and Bayesian for Context-Aware Recommender Systems

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Bayesian Network ◽  
Recommender Systems ◽  
Privacy Protection ◽  
Recommendation System ◽  
Differential Privacy ◽  
Contextual Information ◽  
Network Technology ◽  
Context Aware ◽  
Individual Privacy ◽  
Network Technologies

Incorporate contextual information into recommendation systems can obtain better accuracy of recommendation, however, the users’ individual privacy may be disclosed by attackers. In order to resolve this problem, the authors propose a context-aware recommendation system that integrates Differential Privacy and Bayesian Network technologies (DPBCF). Firstly, the paper uses k-means algorithm to cluster items to relieve sparsity of rating matrix. Next, for the sake of protecting users’ privacy, the paper adds Laplace noises to ratings. And then adopts Bayesian Network technology to calculate the probability that users like a type of item with contextual information. At last, the authors illustrate the experimental evaluations to show that the proposed algorithm can provide a stronger privacy protection while improving the accuracy of recommendations.

scholarly journals Web crawling based context aware recommender system using optimized deep recurrent neural network

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Venugopal Boppana ◽  
P. Sandhya
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Recommender System ◽  
Recommendation System ◽  
Contextual Information ◽  
Recommendation Systems ◽  
User Preferences ◽  
Bald Eagle ◽  
Context Aware ◽  
Deep Recurrent Neural Network

AbstractRecommendation systems are obtaining more attention in various application fields especially e-commerce, social networks and tourism etc. The top items are recommended based on the ability of recommender system which predict the future preference out of the available items. Because of the internet, the people in the current society has too many options that’s why the recommendation system is very essential. The recommendation is achieved by the particular users who predict the ratings for numerous items and recommend those items to other users. Majorly, content and collaborative filtering techniques are employed in typical recommendation systems to find user preferences and provide final recommendations. But, these systems commonly lacks to take growing user preferences in various contextual factors. Context aware recommendation systems consider various contextual parameters into account and attempt to catch user preferences appropriately. The majority of the work in the recommender system domain focuses on increasing the recommendation accuracy by employing several proposed approaches where the main motive remains to maximize the accuracy of recommendations while ignoring other design objectives, such as a user’s an item’s context. Therefore, in this paper an effective deep learning based context aware recommendation model is proposed which can be act as an efficient recommender system by showing minimum error during recommendation. Initially, the dataset is pre-processed using Natural Language Tool Kit (NLTK) in Python platform. After pre-processing, the TF–IDF and word embedding model is used for every pre-processed reviews to extract the features and contextual information. The extracted feature is considered as an input of density based clustering to group the negative, neutral and positive sentiments of user reviews. Finally, deep recurrent neural Network (DRNN) is employed to get the most preferable user from every cluster. The recurrent neural network model parameter values are initialized through the fitness computation of Bald Eagle Search (BES) algorithm. The proposed model is implemented using NYC Restaurant Rich Dataset using Python programming platform and performance is evaluated based on the metrics of accuracy, precision, recall and compared with existing models. The proposed recommendation model achieves 99.6% accuracy which is comparatively higher than other machine learning models.

scholarly journals Context-Aware Neural Machine Translation for Korean Honorific Expressions

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1589
Author(s):  
Yongkeun Hwang ◽  
Yanghoon Kim ◽  
Kyomin Jung
Keyword(s):  
Machine Translation ◽  
Deep Neural Networks ◽  
Contextual Information ◽  
Context Aware ◽  
Neural Machine Translation ◽  
Translation Quality ◽  
Sentence Level ◽  
Proposed Model ◽  
The Given ◽  
The Relationship

Neural machine translation (NMT) is one of the text generation tasks which has achieved significant improvement with the rise of deep neural networks. However, language-specific problems such as handling the translation of honorifics received little attention. In this paper, we propose a context-aware NMT to promote translation improvements of Korean honorifics. By exploiting the information such as the relationship between speakers from the surrounding sentences, our proposed model effectively manages the use of honorific expressions. Specifically, we utilize a novel encoder architecture that can represent the contextual information of the given input sentences. Furthermore, a context-aware post-editing (CAPE) technique is adopted to refine a set of inconsistent sentence-level honorific translations. To demonstrate the efficacy of the proposed method, honorific-labeled test data is required. Thus, we also design a heuristic that labels Korean sentences to distinguish between honorific and non-honorific styles. Experimental results show that our proposed method outperforms sentence-level NMT baselines both in overall translation quality and honorific translations.

scholarly journals Context-aware recommender systems and cultural heritage: a survey

2021 ◽  
Author(s):  
Mario Casillo ◽  
Francesco Colace ◽  
Dajana Conte ◽  
Marco Lombardi ◽  
Domenico Santaniello ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Recommender Systems ◽  
Context Awareness ◽  
Technological Development ◽  
Context Aware ◽  
Human Beings ◽  
Context Analysis ◽  
Analysis Techniques ◽  
The Right ◽  
Technological Improvements

AbstractIn the Big Data era, every sector has adapted to technological development to service the vast amount of information available. In this way, each field has benefited from technological improvements over the years. The cultural and artistic field was no exception, and several studies contributed to the aim of the interaction between human beings and artistic-cultural heritage. In this scenario, systems able to analyze the current situation and recommend the right services play a crucial role. In particular, in the Recommender Systems field, Context-Awareness helps to improve the recommendations provided. This article aims to present a general overview of the introduction of Context analysis techniques in Recommender Systems and discuss some challenging applications to the Cultural Heritage field.

The Selection of MOOCs While Planning a Career of an it Specialist in Russia

2021 ◽  
Vol 27 (2) ◽  
pp. 102-111
Author(s):  
S. M. Avdoshin ◽  
◽  
E. Y. Pesotskaya ◽  
D. M. Kuruppuge ◽  
◽  
...  
Keyword(s):  
Future Development ◽  
Recommendation System ◽  
Career Planning ◽  
User Requirements ◽  
It Professionals ◽  
Modern Approach ◽  
Skill Sets ◽  
Emerging Trends ◽  
Information And Communication ◽  
Selection Of

Digitalization, which has been so much talked about, contributes to the development of many industries in Russia and in the world, but at the same time dictates new requirements for digital personnel and their competencies. To keep pace with emerging trends and information technology and plan for future careers, information and communication technology (ICT) professionals should continually update their skill sets and develop new competencies with the help of the MOOC platforms that suggest appropriate courses. However, given the wide variety of platforms and courses, one can get confused about what to choose for the future development, which courses to take and what profession to follow. The authors conduct a research on user requirements, existing MOOC recommendation systems and their functions, and propose a recommendation system that allows users to select an existing MOOC platform based on assignments and skills for ICT career planning in Russia. The article proposes a modern approach that helps IT professionals plan their future development path based on MOOC recommendations corresponding to their development needs.

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